6.5 C
New York
Thursday, January 20, 2022

Transformational machine learning(TML): ‘learns how to learn’

If you teach a machine learning algorithm what a rabbit looks like, it will be able to tell whether an animal is or isn’t a rabbit.

Must Read

Binge-watching TV increases the risk of serious blood clots by 35%, says new study

"Being physically active does not eliminate the increased risk of blood clots associated with prolonged TV watching,"...

Common Air Pollutants Threaten Butterflies and Bees’ Ability to Find Flowers

The study found up to 70% fewer pollinators, up to 90% fewer flower visits and an overall...

Women’s sports face greater misogynistic attitude from male football fans

A new study published today claims that openly misogynistic attitudes toward women's sport may be common among male...
Jiya Saini
Jiya Saini is a Journalist and Writer at Revyuh.com. She has been working with us since January 2018. After studying at Jamia Millia University, she is fascinated by smart lifestyle and smart living. She covers technology, games, sports and smart living, as well as good experience in press relations. She is also a freelance trainer for macOS and iOS, and In the past, she has worked with various online news magazines in India and Singapore. Email: jiya (at) revyuh (dot) com

By strengthening the machine learning systems that are used to identify new pharmaceuticals, the method, called transformational machine learning (TML) could speed up the identification and production of new drugs.

Engineers have devised a new machine learning approach that ‘learns how to learn’ and outperforms existing machine learning approaches for drug research, potentially speeding up the search for new illness therapies.

A team from the United Kingdom, Sweden, India, and the Netherlands created the transformational machine learning (TML) approach. It learns from a variety of problems and improves its performance while it learns.

By refining the machine learning systems that are used to identify new treatments, TML could speed up the identification and production of new drugs. The findings have been published in Proceedings of the National Academy of Sciences.

Most methods of machine learning (ML) rely on labelled instances, which are virtually usually represented in the computer using inherent properties like an object’s color or shape. After that, the computer creates generic rules that link the features to the labels.

“It’s sort of like teaching a child to identify different animals: this is a rabbit, this is a donkey and so on,” says Professor Ross King, the lead researcher from Cambridge’s Department of Chemical Engineering and Biotechnology.

“If you teach a machine learning algorithm what a rabbit looks like, it will be able to tell whether an animal is or isn’t a rabbit. This is the way that most machine learning works – it deals with problems one at a time.”

Human learning, on the other hand, does not function this way: rather than dealing with a single problem at a time, we improve our ability to learn by applying what we have learned previously.

“To develop TML, we applied this approach to machine learning, and developed a system that learns information from previous problems it has encountered in order to better learn new problems,” says King, who is also a Fellow at The Alan Turing Institute.

“Where a typical ML system has to start from scratch when learning to identify a new type of animal – say a kitten – TML can use the similarity to existing animals: kittens are cute like rabbits, but don’t have long ears like rabbits and donkeys. This makes TML a much more powerful approach to machine learning.”

The researchers tested their theory on hundreds of issues from many fields of science and engineering. They claim it has special promise in the field of drug discovery, because this method speeds up the process by comparing what other machine learning models have to say about a given chemical.

For example, a typical ML technique would look for medicinal compounds with a specific form. TML, on the other hand, makes use of the medications’ connections to other drug discovery difficulties.

“I was surprised how well it works – better than anything else we know for drug design,” says King.

“It’s better at choosing drugs than humans are – and without the best science, we won’t get the best results.”

Source: 10.1073/pnas.2108013118

Image Credit: iStock

You were reading: Transformational machine learning(TML): ‘learns how to learn’

- Advertisement -
- Advertisement -

Latest News

- Advertisement -

More Articles Like This

- Advertisement -